Protein and peptide identification algorithms using MS for use in high-throughput, automated pipelines

Abstract

Current proteomics experiments can generate vast quantities of data very quickly, but this has not been matched by data analysis capabilities. Although there have been a number of recent reviews covering various aspects of peptide and protein identification methods using MS, comparisons of which methods are either the most appropriate for, or the most effective at, their proposed tasks are not readily available. As the need for high-throughput, automated peptide and protein identification systems increases, the creators of such pipelines need to be able to choose algorithms that are going to perform well both in terms of accuracy and computational efficiency. This article therefore provides a review of the currently available core algorithms for PMF, database searching using MS/MS, sequence tag searches and de novo sequencing. We also assess the relative performances of a number of these algorithms. As there is limited reporting of such information in the literature, we conclude that there is a need for the adoption of a system of standardised reporting on the performance of new peptide and protein identification algorithms, based upon freely available datasets. We go on to present our initial suggestions for the format and content of these datasets.

Ranking tandem mass spectra: And the impact of database size and scoring function on peptide spectrum matches

“…One, de novo sequencing, assigns a sequence to the MS/MS spectrum with no additional information [3], while the other (database search) uses a sequence database to select the best matching peptide from a list of expected proteins. Many algorithms have been proposed for both de novo sequencing [3] and database search [4], [5]. In database search, OMSSA [6], X!Tandem [7], and MSGFDB [8] are prominent free tools.…”

Automation, parallelism, and robotics for proteomics

“…Researchers at Cranfield University have developed the Genome Annotating Proteomic Pipeline (GAPP), currently in its beta release [14]. It is a scalable, grid-based pipeline designed to analyze MS data automatically.…”

Existing bioinformatics tools for the quantitation of post-translational modifications

“…Most modern mass spectrometers are capable of several stages of MS and more information can be gained about a particular peptide entering the MS by using a second stage of MS (MS/MS, tandem-MS, MS 2 ) following fragmentation of the peptide, where the fragment ions can form mass ladders in a manner similar to Sanger sequencing for nucleotides. In this fashion the peptide sequence can be determined using data analysis methods like de novo sequencing and database searching (Shadforth et al 2005;Kapp et al 2005). Some mass spectrometers allow additional stages of MS for analysis of the fragments of fragments, which makes further analysis possible.…”